Overview

Dataset statistics

Number of variables28
Number of observations1786867
Missing cells126678
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory276.1 MiB
Average record size in memory162.0 B

Variable types

Numeric16
Categorical10
Text1
DateTime1

Alerts

addr_state has a high cardinality: 51 distinct valuesHigh cardinality
application_type is highly imbalanced (76.5%)Imbalance
emp_title has 126678 (7.1%) missing valuesMissing
acc_now_delinq is highly skewed (γ1 = 21.53739225)Skewed
annual_inc is highly skewed (γ1 = 516.2230369)Skewed
dti is highly skewed (γ1 = 29.7500553)Skewed
acc_now_delinq has 1779026 (99.6%) zerosZeros
bc_util has 23438 (1.3%) zerosZeros
delinq_2yrs has 1444417 (80.8%) zerosZeros
inq_last_6mths has 1061742 (59.4%) zerosZeros
pub_rec has 1483020 (83.0%) zerosZeros
pub_rec_bankruptcies has 1558818 (87.2%) zerosZeros

Reproduction

Analysis started2024-07-10 03:22:50.003932
Analysis finished2024-07-10 03:23:27.562539
Duration37.56 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

acc_now_delinq
Real number (ℝ)

SKEWED  ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0046869745
Minimum0
Maximum14
Zeros1779026
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:27.585972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum14
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.073950328
Coefficient of variation (CV)15.777839
Kurtosis1143.1848
Mean0.0046869745
Median Absolute Deviation (MAD)0
Skewness21.537392
Sum8375
Variance0.005468651
MonotonicityNot monotonic
2024-07-09T22:23:27.621841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1779026
99.6%
1 7399
 
0.4%
2 381
 
< 0.1%
3 45
 
< 0.1%
4 11
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
0 1779026
99.6%
1 7399
 
0.4%
2 381
 
< 0.1%
3 45
 
< 0.1%
4 11
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
6 1
 
< 0.1%
5 3
 
< 0.1%
4 11
 
< 0.1%
3 45
 
< 0.1%
2 381
 
< 0.1%
1 7399
 
0.4%
0 1779026
99.6%

addr_state
Categorical

HIGH CARDINALITY 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
CA
251979 
TX
147621 
NY
144564 
FL
128673 
IL
 
70007
Other values (46)
1044023 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3573734
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNC
2nd rowTX
3rd rowMI
4th rowTX
5th rowNC

Common Values

ValueCountFrequency (%)
CA 251979
 
14.1%
TX 147621
 
8.3%
NY 144564
 
8.1%
FL 128673
 
7.2%
IL 70007
 
3.9%
NJ 64130
 
3.6%
PA 60061
 
3.4%
OH 58818
 
3.3%
GA 58086
 
3.3%
NC 50071
 
2.8%
Other values (41) 752857
42.1%

Length

2024-07-09T22:23:27.660797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 251979
 
14.1%
tx 147621
 
8.3%
ny 144564
 
8.1%
fl 128673
 
7.2%
il 70007
 
3.9%
nj 64130
 
3.6%
pa 60061
 
3.4%
oh 58818
 
3.3%
ga 58086
 
3.3%
nc 50071
 
2.8%
Other values (41) 752857
42.1%

Most occurring characters

ValueCountFrequency (%)
A 601485
16.8%
N 404030
11.3%
C 393768
11.0%
L 240559
 
6.7%
T 224948
 
6.3%
M 217813
 
6.1%
I 191235
 
5.4%
Y 165597
 
4.6%
O 164254
 
4.6%
X 147621
 
4.1%
Other values (14) 822424
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 601485
16.8%
N 404030
11.3%
C 393768
11.0%
L 240559
 
6.7%
T 224948
 
6.3%
M 217813
 
6.1%
I 191235
 
5.4%
Y 165597
 
4.6%
O 164254
 
4.6%
X 147621
 
4.1%
Other values (14) 822424
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 601485
16.8%
N 404030
11.3%
C 393768
11.0%
L 240559
 
6.7%
T 224948
 
6.3%
M 217813
 
6.1%
I 191235
 
5.4%
Y 165597
 
4.6%
O 164254
 
4.6%
X 147621
 
4.1%
Other values (14) 822424
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 601485
16.8%
N 404030
11.3%
C 393768
11.0%
L 240559
 
6.7%
T 224948
 
6.3%
M 217813
 
6.1%
I 191235
 
5.4%
Y 165597
 
4.6%
O 164254
 
4.6%
X 147621
 
4.1%
Other values (14) 822424
23.0%

annual_inc
Real number (ℝ)

SKEWED 

Distinct76713
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77860.225
Minimum0
Maximum1.1 × 108
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:27.701608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28000
Q146500
median65000
Q393000
95-th percentile160000
Maximum1.1 × 108
Range1.1 × 108
Interquartile range (IQR)46500

Descriptive statistics

Standard deviation119837.41
Coefficient of variation (CV)1.5391351
Kurtosis434186.59
Mean77860.225
Median Absolute Deviation (MAD)21699
Skewness516.22304
Sum1.3912587 × 1011
Variance1.4361004 × 1010
MonotonicityNot monotonic
2024-07-09T22:23:27.750503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 69034
 
3.9%
50000 60705
 
3.4%
65000 52069
 
2.9%
70000 49607
 
2.8%
80000 47525
 
2.7%
40000 47157
 
2.6%
75000 46419
 
2.6%
45000 43560
 
2.4%
55000 41464
 
2.3%
100000 37094
 
2.1%
Other values (76703) 1292233
72.3%
ValueCountFrequency (%)
0 4
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
20 2
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
39 1
 
< 0.1%
50 1
 
< 0.1%
ValueCountFrequency (%)
110000000 1
< 0.1%
61000000 1
< 0.1%
10999200 1
< 0.1%
9573072 1
< 0.1%
9550000 1
< 0.1%
9522972 1
< 0.1%
9500000 1
< 0.1%
9300000 1
< 0.1%
9225000 1
< 0.1%
9100000 1
< 0.1%

application_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Individual
1718155 
Joint App
 
68712

Length

Max length10
Median length10
Mean length9.9615461
Min length9

Characters and Unicode

Total characters17799958
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 1718155
96.2%
Joint App 68712
 
3.8%

Length

2024-07-09T22:23:27.793043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T22:23:27.830533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 1718155
92.6%
joint 68712
 
3.7%
app 68712
 
3.7%

Most occurring characters

ValueCountFrequency (%)
i 3505022
19.7%
d 3436310
19.3%
n 1786867
10.0%
I 1718155
9.7%
v 1718155
9.7%
u 1718155
9.7%
a 1718155
9.7%
l 1718155
9.7%
p 137424
 
0.8%
J 68712
 
0.4%
Other values (4) 274848
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17799958
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3505022
19.7%
d 3436310
19.3%
n 1786867
10.0%
I 1718155
9.7%
v 1718155
9.7%
u 1718155
9.7%
a 1718155
9.7%
l 1718155
9.7%
p 137424
 
0.8%
J 68712
 
0.4%
Other values (4) 274848
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17799958
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3505022
19.7%
d 3436310
19.3%
n 1786867
10.0%
I 1718155
9.7%
v 1718155
9.7%
u 1718155
9.7%
a 1718155
9.7%
l 1718155
9.7%
p 137424
 
0.8%
J 68712
 
0.4%
Other values (4) 274848
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17799958
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3505022
19.7%
d 3436310
19.3%
n 1786867
10.0%
I 1718155
9.7%
v 1718155
9.7%
u 1718155
9.7%
a 1718155
9.7%
l 1718155
9.7%
p 137424
 
0.8%
J 68712
 
0.4%
Other values (4) 274848
 
1.5%

avg_cur_bal
Real number (ℝ)

Distinct83451
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13459.29
Minimum0
Maximum555925
Zeros624
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:27.871327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1078
Q13077
median7346
Q318709
95-th percentile43141
Maximum555925
Range555925
Interquartile range (IQR)15632

Descriptive statistics

Standard deviation16157.496
Coefficient of variation (CV)1.2004716
Kurtosis28.751143
Mean13459.29
Median Absolute Deviation (MAD)5375
Skewness3.4881532
Sum2.4049962 × 1010
Variance2.6106468 × 108
MonotonicityNot monotonic
2024-07-09T22:23:27.918315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 624
 
< 0.1%
1971 232
 
< 0.1%
2253 228
 
< 0.1%
2076 226
 
< 0.1%
2447 224
 
< 0.1%
2301 223
 
< 0.1%
2149 222
 
< 0.1%
2842 222
 
< 0.1%
2606 220
 
< 0.1%
2079 220
 
< 0.1%
Other values (83441) 1784226
99.9%
ValueCountFrequency (%)
0 624
< 0.1%
1 53
 
< 0.1%
2 48
 
< 0.1%
3 51
 
< 0.1%
4 26
 
< 0.1%
5 45
 
< 0.1%
6 38
 
< 0.1%
7 29
 
< 0.1%
8 30
 
< 0.1%
9 32
 
< 0.1%
ValueCountFrequency (%)
555925 1
< 0.1%
502002 1
< 0.1%
498284 1
< 0.1%
497484 1
< 0.1%
478909 1
< 0.1%
477255 1
< 0.1%
466840 1
< 0.1%
463945 1
< 0.1%
463276 1
< 0.1%
447102 1
< 0.1%

bc_util
Real number (ℝ)

ZEROS 

Distinct1476
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.043113
Minimum0
Maximum339.6
Zeros23438
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:27.965727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4
Q135.5
median60.6
Q383.4
95-th percentile97.9
Maximum339.6
Range339.6
Interquartile range (IQR)47.9

Descriptive statistics

Standard deviation28.710216
Coefficient of variation (CV)0.49463604
Kurtosis-0.99604539
Mean58.043113
Median Absolute Deviation (MAD)23.8
Skewness-0.28097288
Sum1.0371532 × 108
Variance824.27647
MonotonicityNot monotonic
2024-07-09T22:23:28.016157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23438
 
1.3%
98 4968
 
0.3%
97 4671
 
0.3%
99 4595
 
0.3%
96 4528
 
0.3%
95 4191
 
0.2%
94 3985
 
0.2%
93 3797
 
0.2%
92 3684
 
0.2%
91 3555
 
0.2%
Other values (1466) 1725455
96.6%
ValueCountFrequency (%)
0 23438
1.3%
0.1 1959
 
0.1%
0.2 1709
 
0.1%
0.3 1449
 
0.1%
0.4 1253
 
0.1%
0.5 1198
 
0.1%
0.6 1092
 
0.1%
0.7 1076
 
0.1%
0.8 1031
 
0.1%
0.9 971
 
0.1%
ValueCountFrequency (%)
339.6 1
< 0.1%
318.2 1
< 0.1%
255.2 1
< 0.1%
252.3 1
< 0.1%
243.8 1
< 0.1%
235.3 1
< 0.1%
204.6 1
< 0.1%
202.9 1
< 0.1%
202 1
< 0.1%
201.9 1
< 0.1%

delinq_2yrs
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31676112
Minimum0
Maximum39
Zeros1444417
Zeros (%)80.8%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:28.062410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88076313
Coefficient of variation (CV)2.7805279
Kurtosis60.149821
Mean0.31676112
Median Absolute Deviation (MAD)0
Skewness5.673827
Sum566010
Variance0.7757437
MonotonicityNot monotonic
2024-07-09T22:23:28.108612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 1444417
80.8%
1 227702
 
12.7%
2 66278
 
3.7%
3 24237
 
1.4%
4 10843
 
0.6%
5 5454
 
0.3%
6 3099
 
0.2%
7 1710
 
0.1%
8 1050
 
0.1%
9 651
 
< 0.1%
Other values (24) 1426
 
0.1%
ValueCountFrequency (%)
0 1444417
80.8%
1 227702
 
12.7%
2 66278
 
3.7%
3 24237
 
1.4%
4 10843
 
0.6%
5 5454
 
0.3%
6 3099
 
0.2%
7 1710
 
0.1%
8 1050
 
0.1%
9 651
 
< 0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
36 1
 
< 0.1%
32 1
 
< 0.1%
30 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
< 0.1%
25 2
< 0.1%
24 1
 
< 0.1%

dti
Real number (ℝ)

SKEWED 

Distinct9396
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.753862
Minimum-1
Maximum999
Zeros984
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size27.3 MiB
2024-07-09T22:23:28.158518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5.06
Q111.97
median17.88
Q324.52
95-th percentile33.61
Maximum999
Range1000
Interquartile range (IQR)12.55

Descriptive statistics

Standard deviation13.283835
Coefficient of variation (CV)0.70832532
Kurtosis1925.3093
Mean18.753862
Median Absolute Deviation (MAD)6.24
Skewness29.750055
Sum33510657
Variance176.46028
MonotonicityNot monotonic
2024-07-09T22:23:28.205588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1282
 
0.1%
14.4 1262
 
0.1%
18 1254
 
0.1%
16.8 1247
 
0.1%
13.2 1197
 
0.1%
15.6 1195
 
0.1%
20.4 1164
 
0.1%
12 1151
 
0.1%
21.6 1126
 
0.1%
10.8 1061
 
0.1%
Other values (9386) 1774928
99.3%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 984
0.1%
0.01 12
 
< 0.1%
0.02 24
 
< 0.1%
0.03 14
 
< 0.1%
0.04 9
 
< 0.1%
0.05 15
 
< 0.1%
0.06 28
 
< 0.1%
0.07 20
 
< 0.1%
0.08 22
 
< 0.1%
ValueCountFrequency (%)
999 90
< 0.1%
994.4 1
 
< 0.1%
991.57 1
 
< 0.1%
962.83 1
 
< 0.1%
962.12 1
 
< 0.1%
942.17 1
 
< 0.1%
917.87 1
 
< 0.1%
893.1 1
 
< 0.1%
886.77 1
 
< 0.1%
879.55 1
 
< 0.1%

emp_length
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
10
592270 
0
260701 
2
161222 
3
142893 
1
117587 
Other values (6)
512194 

Length

Max length2
Median length1
Mean length1.3314572
Min length1

Characters and Unicode

Total characters2379137
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row10
4th row3
5th row4

Common Values

ValueCountFrequency (%)
10 592270
33.1%
0 260701
14.6%
2 161222
 
9.0%
3 142893
 
8.0%
1 117587
 
6.6%
5 109864
 
6.1%
4 106403
 
6.0%
6 80982
 
4.5%
8 75735
 
4.2%
7 74552
 
4.2%

Length

2024-07-09T22:23:28.247716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10 592270
33.1%
0 260701
14.6%
2 161222
 
9.0%
3 142893
 
8.0%
1 117587
 
6.6%
5 109864
 
6.1%
4 106403
 
6.0%
6 80982
 
4.5%
8 75735
 
4.2%
7 74552
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 852971
35.9%
1 709857
29.8%
2 161222
 
6.8%
3 142893
 
6.0%
5 109864
 
4.6%
4 106403
 
4.5%
6 80982
 
3.4%
8 75735
 
3.2%
7 74552
 
3.1%
9 64658
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2379137
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 852971
35.9%
1 709857
29.8%
2 161222
 
6.8%
3 142893
 
6.0%
5 109864
 
4.6%
4 106403
 
4.5%
6 80982
 
3.4%
8 75735
 
3.2%
7 74552
 
3.1%
9 64658
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2379137
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 852971
35.9%
1 709857
29.8%
2 161222
 
6.8%
3 142893
 
6.0%
5 109864
 
4.6%
4 106403
 
4.5%
6 80982
 
3.4%
8 75735
 
3.2%
7 74552
 
3.1%
9 64658
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2379137
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 852971
35.9%
1 709857
29.8%
2 161222
 
6.8%
3 142893
 
6.0%
5 109864
 
4.6%
4 106403
 
4.5%
6 80982
 
3.4%
8 75735
 
3.2%
7 74552
 
3.1%
9 64658
 
2.7%

emp_title
Text

MISSING 

Distinct355971
Distinct (%)21.4%
Missing126678
Missing (%)7.1%
Memory size27.3 MiB
2024-07-09T22:23:28.395790image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length15.478738
Min length1

Characters and Unicode

Total characters25697630
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique269961 ?
Unique (%)16.3%

Sample

1st rowPROJECT_MANAGER
2nd rowSURGICAL_TECHNICIAN
3rd rowTEAM_LEADERN_CUSTOMER_OPS_&_SYSTEMS
4th rowSYSTEMS_ENGINEER
5th rowASSISTANT_DIRECTOR_-_HUMAN_RESOURCES
ValueCountFrequency (%)
teacher 38171
 
2.3%
manager 37532
 
2.3%
nurse 36542
 
2.2%
owner 24817
 
1.5%
supervisor 18084
 
1.1%
driver 18050
 
1.1%
sales 15634
 
0.9%
office_manager 11337
 
0.7%
project_manager 11231
 
0.7%
general_manager 10765
 
0.6%
Other values (332524) 1438045
86.6%
2024-07-09T22:23:28.613219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2939401
11.4%
R 2403245
9.4%
A 2383501
9.3%
I 1924834
 
7.5%
N 1870482
 
7.3%
_ 1847705
 
7.2%
T 1809960
 
7.0%
S 1714660
 
6.7%
O 1449712
 
5.6%
C 1398838
 
5.4%
Other values (121) 5955292
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25697630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2939401
11.4%
R 2403245
9.4%
A 2383501
9.3%
I 1924834
 
7.5%
N 1870482
 
7.3%
_ 1847705
 
7.2%
T 1809960
 
7.0%
S 1714660
 
6.7%
O 1449712
 
5.6%
C 1398838
 
5.4%
Other values (121) 5955292
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25697630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2939401
11.4%
R 2403245
9.4%
A 2383501
9.3%
I 1924834
 
7.5%
N 1870482
 
7.3%
_ 1847705
 
7.2%
T 1809960
 
7.0%
S 1714660
 
6.7%
O 1449712
 
5.6%
C 1398838
 
5.4%
Other values (121) 5955292
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25697630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2939401
11.4%
R 2403245
9.4%
A 2383501
9.3%
I 1924834
 
7.5%
N 1870482
 
7.3%
_ 1847705
 
7.2%
T 1809960
 
7.0%
S 1714660
 
6.7%
O 1449712
 
5.6%
C 1398838
 
5.4%
Other values (121) 5955292
23.2%

fico_range_high
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.17936
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:28.673630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1674
median694
Q3719
95-th percentile769
Maximum850
Range186
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.47601
Coefficient of variation (CV)0.046316267
Kurtosis1.5491764
Mean701.17936
Median Absolute Deviation (MAD)20
Skewness1.2591692
Sum1.2529143 × 109
Variance1054.6913
MonotonicityNot monotonic
2024-07-09T22:23:28.717582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
664 156140
 
8.7%
674 151186
 
8.5%
669 150637
 
8.4%
684 135122
 
7.6%
679 135050
 
7.6%
689 118766
 
6.6%
694 115689
 
6.5%
699 103958
 
5.8%
704 97075
 
5.4%
709 88066
 
4.9%
Other values (28) 535178
30.0%
ValueCountFrequency (%)
664 156140
8.7%
669 150637
8.4%
674 151186
8.5%
679 135050
7.6%
684 135122
7.6%
689 118766
6.6%
694 115689
6.5%
699 103958
5.8%
704 97075
5.4%
709 88066
4.9%
ValueCountFrequency (%)
850 306
 
< 0.1%
844 400
 
< 0.1%
839 623
 
< 0.1%
834 1077
 
0.1%
829 1611
 
0.1%
824 2128
 
0.1%
819 2924
0.2%
814 3470
0.2%
809 4846
0.3%
804 5724
0.3%

fico_range_low
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.17919
Minimum660
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:28.760120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile660
Q1670
median690
Q3715
95-th percentile765
Maximum845
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.475228
Coefficient of variation (CV)0.046580892
Kurtosis1.5476317
Mean697.17919
Median Absolute Deviation (MAD)20
Skewness1.258946
Sum1.2457665 × 109
Variance1054.6405
MonotonicityNot monotonic
2024-07-09T22:23:28.803497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
660 156140
 
8.7%
670 151186
 
8.5%
665 150637
 
8.4%
680 135122
 
7.6%
675 135050
 
7.6%
685 118766
 
6.6%
690 115689
 
6.5%
695 103958
 
5.8%
700 97075
 
5.4%
705 88066
 
4.9%
Other values (28) 535178
30.0%
ValueCountFrequency (%)
660 156140
8.7%
665 150637
8.4%
670 151186
8.5%
675 135050
7.6%
680 135122
7.6%
685 118766
6.6%
690 115689
6.5%
695 103958
5.8%
700 97075
5.4%
705 88066
4.9%
ValueCountFrequency (%)
845 306
 
< 0.1%
840 400
 
< 0.1%
835 623
 
< 0.1%
830 1077
 
0.1%
825 1611
 
0.1%
820 2128
 
0.1%
815 2924
0.2%
810 3470
0.2%
805 4846
0.3%
800 5724
0.3%

grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
B
523797 
C
512850 
A
329095 
D
265392 
E
110811 
Other values (2)
 
44922

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1786867
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B 523797
29.3%
C 512850
28.7%
A 329095
18.4%
D 265392
14.9%
E 110811
 
6.2%
F 34874
 
2.0%
G 10048
 
0.6%

Length

2024-07-09T22:23:28.844866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T22:23:28.881863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
b 523797
29.3%
c 512850
28.7%
a 329095
18.4%
d 265392
14.9%
e 110811
 
6.2%
f 34874
 
2.0%
g 10048
 
0.6%

Most occurring characters

ValueCountFrequency (%)
B 523797
29.3%
C 512850
28.7%
A 329095
18.4%
D 265392
14.9%
E 110811
 
6.2%
F 34874
 
2.0%
G 10048
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1786867
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 523797
29.3%
C 512850
28.7%
A 329095
18.4%
D 265392
14.9%
E 110811
 
6.2%
F 34874
 
2.0%
G 10048
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1786867
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 523797
29.3%
C 512850
28.7%
A 329095
18.4%
D 265392
14.9%
E 110811
 
6.2%
F 34874
 
2.0%
G 10048
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1786867
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 523797
29.3%
C 512850
28.7%
A 329095
18.4%
D 265392
14.9%
E 110811
 
6.2%
F 34874
 
2.0%
G 10048
 
0.6%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
MORTGAGE
883347 
RENT
702476 
OWN
199747 
ANY
 
1208
NONE
 
45

Length

Max length8
Median length5
Mean length5.8649832
Min length3

Characters and Unicode

Total characters10479945
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowMORTGAGE
3rd rowOWN
4th rowMORTGAGE
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE 883347
49.4%
RENT 702476
39.3%
OWN 199747
 
11.2%
ANY 1208
 
0.1%
NONE 45
 
< 0.1%
OTHER 44
 
< 0.1%

Length

2024-07-09T22:23:28.924158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T22:23:28.961348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 883347
49.4%
rent 702476
39.3%
own 199747
 
11.2%
any 1208
 
0.1%
none 45
 
< 0.1%
other 44
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 1766694
16.9%
E 1585912
15.1%
R 1585867
15.1%
T 1585867
15.1%
O 1083183
10.3%
N 903521
8.6%
A 884555
8.4%
M 883347
8.4%
W 199747
 
1.9%
Y 1208
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10479945
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1766694
16.9%
E 1585912
15.1%
R 1585867
15.1%
T 1585867
15.1%
O 1083183
10.3%
N 903521
8.6%
A 884555
8.4%
M 883347
8.4%
W 199747
 
1.9%
Y 1208
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10479945
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1766694
16.9%
E 1585912
15.1%
R 1585867
15.1%
T 1585867
15.1%
O 1083183
10.3%
N 903521
8.6%
A 884555
8.4%
M 883347
8.4%
W 199747
 
1.9%
Y 1208
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10479945
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1766694
16.9%
E 1585912
15.1%
R 1585867
15.1%
T 1585867
15.1%
O 1083183
10.3%
N 903521
8.6%
A 884555
8.4%
M 883347
8.4%
W 199747
 
1.9%
Y 1208
 
< 0.1%

inq_last_6mths
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60544126
Minimum0
Maximum8
Zeros1061742
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:28.997828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89568327
Coefficient of variation (CV)1.4793892
Kurtosis3.5645006
Mean0.60544126
Median Absolute Deviation (MAD)0
Skewness1.7586611
Sum1081843
Variance0.80224853
MonotonicityNot monotonic
2024-07-09T22:23:29.033964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1061742
59.4%
1 477811
26.7%
2 167065
 
9.3%
3 58273
 
3.3%
4 15665
 
0.9%
5 5448
 
0.3%
6 859
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 1061742
59.4%
1 477811
26.7%
2 167065
 
9.3%
3 58273
 
3.3%
4 15665
 
0.9%
5 5448
 
0.3%
6 859
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 3
 
< 0.1%
6 859
 
< 0.1%
5 5448
 
0.3%
4 15665
 
0.9%
3 58273
 
3.3%
2 167065
 
9.3%
1 477811
26.7%
0 1061742
59.4%

installment
Real number (ℝ)

Distinct88618
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.74625
Minimum4.93
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:29.077030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile109.86
Q1250.33
median377.04
Q3591.205
95-th percentile988.42
Maximum1719.83
Range1714.9
Interquartile range (IQR)340.875

Descriptive statistics

Standard deviation267.81855
Coefficient of variation (CV)0.60218281
Kurtosis0.70534339
Mean444.74625
Median Absolute Deviation (MAD)158
Skewness1.0095919
Sum7.9470239 × 108
Variance71726.773
MonotonicityNot monotonic
2024-07-09T22:23:29.124662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.15 3862
 
0.2%
332.1 3382
 
0.2%
327.34 3281
 
0.2%
361.38 3173
 
0.2%
602.3 2739
 
0.2%
451.73 2738
 
0.2%
329.72 2562
 
0.1%
318.79 2280
 
0.1%
312.86 2181
 
0.1%
392.81 2168
 
0.1%
Other values (88608) 1758501
98.4%
ValueCountFrequency (%)
4.93 1
< 0.1%
7.61 1
< 0.1%
14.01 1
< 0.1%
14.77 1
< 0.1%
19.4 1
< 0.1%
20.11 1
< 0.1%
23.26 1
< 0.1%
23.36 1
< 0.1%
25.81 1
< 0.1%
25.86 1
< 0.1%
ValueCountFrequency (%)
1719.83 2
< 0.1%
1717.63 1
 
< 0.1%
1715.42 2
< 0.1%
1714.54 4
< 0.1%
1691.28 2
< 0.1%
1676.23 2
< 0.1%
1671.88 2
< 0.1%
1670.15 1
 
< 0.1%
1664.57 1
 
< 0.1%
1647.03 1
 
< 0.1%

int_rate
Real number (ℝ)

Distinct395
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13194428
Minimum0.0531
Maximum0.3099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:29.351416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0531
5-th percentile0.0649
Q10.0949
median0.1269
Q30.1599
95-th percentile0.2239
Maximum0.3099
Range0.2568
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.048550742
Coefficient of variation (CV)0.36796397
Kurtosis0.54913972
Mean0.13194428
Median Absolute Deviation (MAD)0.0325
Skewness0.75727745
Sum235766.89
Variance0.0023571746
MonotonicityNot monotonic
2024-07-09T22:23:29.399924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1199 41995
 
2.4%
0.0532 41651
 
2.3%
0.1099 41309
 
2.3%
0.1399 36842
 
2.1%
0.1149 29489
 
1.7%
0.1699 28203
 
1.6%
0.1299 27678
 
1.5%
0.0789 27372
 
1.5%
0.0917 26929
 
1.5%
0.1561 24268
 
1.4%
Other values (385) 1461131
81.8%
ValueCountFrequency (%)
0.0531 3301
 
0.2%
0.0532 41651
2.3%
0.0593 1809
 
0.1%
0.06 602
 
< 0.1%
0.0603 9250
 
0.5%
0.0607 2234
 
0.1%
0.0608 2947
 
0.2%
0.0611 3854
 
0.2%
0.0619 1330
 
0.1%
0.0624 7456
 
0.4%
ValueCountFrequency (%)
0.3099 667
< 0.1%
0.3094 574
< 0.1%
0.3089 544
< 0.1%
0.3084 609
< 0.1%
0.3079 1165
0.1%
0.3075 755
< 0.1%
0.3074 394
 
< 0.1%
0.3065 697
< 0.1%
0.3049 446
 
< 0.1%
0.3017 846
< 0.1%
Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.3 MiB
Minimum2012-08-01 00:00:00
Maximum2020-09-01 00:00:00
2024-07-09T22:23:29.447879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:29.499850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

loan_amnt
Real number (ℝ)

Distinct1561
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14740.787
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:29.549101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3200
Q18000
median12125
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9002.6375
Coefficient of variation (CV)0.61072978
Kurtosis-0.087397524
Mean14740.787
Median Absolute Deviation (MAD)5875
Skewness0.78892546
Sum2.6339825 × 1010
Variance81047481
MonotonicityNot monotonic
2024-07-09T22:23:29.602481image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 140497
 
7.9%
20000 98004
 
5.5%
15000 94961
 
5.3%
12000 94778
 
5.3%
35000 67528
 
3.8%
5000 66598
 
3.7%
8000 61975
 
3.5%
6000 58480
 
3.3%
16000 50476
 
2.8%
25000 48353
 
2.7%
Other values (1551) 1005217
56.3%
ValueCountFrequency (%)
1000 7873
0.4%
1025 30
 
< 0.1%
1050 45
 
< 0.1%
1075 22
 
< 0.1%
1100 225
 
< 0.1%
1125 38
 
< 0.1%
1150 33
 
< 0.1%
1175 15
 
< 0.1%
1200 3106
 
0.2%
1225 16
 
< 0.1%
ValueCountFrequency (%)
40000 18897
1.1%
39975 10
 
< 0.1%
39950 5
 
< 0.1%
39925 6
 
< 0.1%
39900 13
 
< 0.1%
39875 5
 
< 0.1%
39850 4
 
< 0.1%
39825 10
 
< 0.1%
39800 10
 
< 0.1%
39775 10
 
< 0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Fully Paid
1422806 
Non-Performing
364061 

Length

Max length14
Median length10
Mean length10.814971
Min length10

Characters and Unicode

Total characters19324914
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 1422806
79.6%
Non-Performing 364061
 
20.4%

Length

2024-07-09T22:23:29.651453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T22:23:29.690776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
fully 1422806
44.3%
paid 1422806
44.3%
non-performing 364061
 
11.3%

Most occurring characters

ValueCountFrequency (%)
l 2845612
14.7%
P 1786867
9.2%
i 1786867
9.2%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 728122
 
3.8%
Other values (8) 3640610
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19324914
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2845612
14.7%
P 1786867
9.2%
i 1786867
9.2%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 728122
 
3.8%
Other values (8) 3640610
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19324914
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2845612
14.7%
P 1786867
9.2%
i 1786867
9.2%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 728122
 
3.8%
Other values (8) 3640610
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19324914
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2845612
14.7%
P 1786867
9.2%
i 1786867
9.2%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 728122
 
3.8%
Other values (8) 3640610
18.8%

pub_rec
Real number (ℝ)

ZEROS 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21426721
Minimum0
Maximum86
Zeros1483020
Zeros (%)83.0%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:29.729872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum86
Range86
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.59843255
Coefficient of variation (CV)2.7929265
Kurtosis723.40073
Mean0.21426721
Median Absolute Deviation (MAD)0
Skewness11.642548
Sum382867
Variance0.35812151
MonotonicityNot monotonic
2024-07-09T22:23:29.772983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 1483020
83.0%
1 256949
 
14.4%
2 30575
 
1.7%
3 9431
 
0.5%
4 3440
 
0.2%
5 1648
 
0.1%
6 827
 
< 0.1%
7 377
 
< 0.1%
8 212
 
< 0.1%
9 118
 
< 0.1%
Other values (32) 270
 
< 0.1%
ValueCountFrequency (%)
0 1483020
83.0%
1 256949
 
14.4%
2 30575
 
1.7%
3 9431
 
0.5%
4 3440
 
0.2%
5 1648
 
0.1%
6 827
 
< 0.1%
7 377
 
< 0.1%
8 212
 
< 0.1%
9 118
 
< 0.1%
ValueCountFrequency (%)
86 1
< 0.1%
63 1
< 0.1%
61 2
< 0.1%
54 1
< 0.1%
52 1
< 0.1%
49 2
< 0.1%
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%

pub_rec_bankruptcies
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1367343
Minimum0
Maximum12
Zeros1558818
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:29.808908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.37780133
Coefficient of variation (CV)2.7630327
Kurtosis18.440094
Mean0.1367343
Median Absolute Deviation (MAD)0
Skewness3.3223977
Sum244326
Variance0.14273385
MonotonicityNot monotonic
2024-07-09T22:23:29.846166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 1558818
87.2%
1 215686
 
12.1%
2 9645
 
0.5%
3 1945
 
0.1%
4 502
 
< 0.1%
5 178
 
< 0.1%
6 58
 
< 0.1%
7 21
 
< 0.1%
8 9
 
< 0.1%
9 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 1558818
87.2%
1 215686
 
12.1%
2 9645
 
0.5%
3 1945
 
0.1%
4 502
 
< 0.1%
5 178
 
< 0.1%
6 58
 
< 0.1%
7 21
 
< 0.1%
8 9
 
< 0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
11 1
 
< 0.1%
9 3
 
< 0.1%
8 9
 
< 0.1%
7 21
 
< 0.1%
6 58
 
< 0.1%
5 178
 
< 0.1%
4 502
 
< 0.1%
3 1945
 
0.1%
2 9645
0.5%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
debt_consolidation
1027147 
credit_card
403867 
home_improvement
117900 
other
105842 
major_purchase
 
38359
Other values (9)
 
93752

Length

Max length18
Median length18
Mean length14.875992
Min length3

Characters and Unicode

Total characters26581420
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowhome_improvement
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation 1027147
57.5%
credit_card 403867
 
22.6%
home_improvement 117900
 
6.6%
other 105842
 
5.9%
major_purchase 38359
 
2.1%
medical 21173
 
1.2%
car 17905
 
1.0%
small_business 17634
 
1.0%
vacation 12503
 
0.7%
moving 12158
 
0.7%
Other values (4) 12379
 
0.7%

Length

2024-07-09T22:23:29.889713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 1027147
57.5%
credit_card 403867
 
22.6%
home_improvement 117900
 
6.6%
other 105842
 
5.9%
major_purchase 38359
 
2.1%
medical 21173
 
1.2%
car 17905
 
1.0%
small_business 17634
 
1.0%
vacation 12503
 
0.7%
moving 12158
 
0.7%
Other values (4) 12379
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 3496508
13.2%
d 2884935
10.9%
t 2694408
10.1%
i 2640397
9.9%
n 2217573
8.3%
e 1984533
7.5%
c 1924823
7.2%
_ 1606015
 
6.0%
a 1590562
 
6.0%
s 1146445
 
4.3%
Other values (12) 4395221
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26581420
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3496508
13.2%
d 2884935
10.9%
t 2694408
10.1%
i 2640397
9.9%
n 2217573
8.3%
e 1984533
7.5%
c 1924823
7.2%
_ 1606015
 
6.0%
a 1590562
 
6.0%
s 1146445
 
4.3%
Other values (12) 4395221
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26581420
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3496508
13.2%
d 2884935
10.9%
t 2694408
10.1%
i 2640397
9.9%
n 2217573
8.3%
e 1984533
7.5%
c 1924823
7.2%
_ 1606015
 
6.0%
a 1590562
 
6.0%
s 1146445
 
4.3%
Other values (12) 4395221
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26581420
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3496508
13.2%
d 2884935
10.9%
t 2694408
10.1%
i 2640397
9.9%
n 2217573
8.3%
e 1984533
7.5%
c 1924823
7.2%
_ 1606015
 
6.0%
a 1590562
 
6.0%
s 1146445
 
4.3%
Other values (12) 4395221
16.5%

revol_bal
Real number (ℝ)

Distinct93558
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16471.303
Minimum0
Maximum2904836
Zeros6822
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:29.936120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1705
Q15967
median11205
Q319950
95-th percentile44318
Maximum2904836
Range2904836
Interquartile range (IQR)13983

Descriptive statistics

Standard deviation22641.686
Coefficient of variation (CV)1.3746141
Kurtosis576.46018
Mean16471.303
Median Absolute Deviation (MAD)6227
Skewness12.566874
Sum2.9432028 × 1010
Variance5.1264594 × 108
MonotonicityNot monotonic
2024-07-09T22:23:29.985466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6822
 
0.4%
8 174
 
< 0.1%
6312 139
 
< 0.1%
5453 132
 
< 0.1%
10 130
 
< 0.1%
2 130
 
< 0.1%
5232 128
 
< 0.1%
5891 128
 
< 0.1%
5570 128
 
< 0.1%
4427 127
 
< 0.1%
Other values (93548) 1778829
99.6%
ValueCountFrequency (%)
0 6822
0.4%
1 93
 
< 0.1%
2 130
 
< 0.1%
3 123
 
< 0.1%
4 115
 
< 0.1%
5 108
 
< 0.1%
6 116
 
< 0.1%
7 98
 
< 0.1%
8 174
 
< 0.1%
9 119
 
< 0.1%
ValueCountFrequency (%)
2904836 1
< 0.1%
2568995 1
< 0.1%
2560703 1
< 0.1%
1746716 1
< 0.1%
1743266 1
< 0.1%
1696796 1
< 0.1%
1470945 1
< 0.1%
1392002 1
< 0.1%
1298783 1
< 0.1%
1190046 1
< 0.1%

revol_util
Real number (ℝ)

Distinct1305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50390201
Minimum0
Maximum3.666
Zeros8308
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size27.3 MiB
2024-07-09T22:23:30.033543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.096
Q10.317
median0.504
Q30.693
95-th percentile0.909
Maximum3.666
Range3.666
Interquartile range (IQR)0.376

Descriptive statistics

Standard deviation0.24608243
Coefficient of variation (CV)0.48835374
Kurtosis-0.80744199
Mean0.50390201
Median Absolute Deviation (MAD)0.188
Skewness-0.0066285781
Sum900405.87
Variance0.060556563
MonotonicityNot monotonic
2024-07-09T22:23:30.084535image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8308
 
0.5%
0.57 3472
 
0.2%
0.48 3470
 
0.2%
0.59 3438
 
0.2%
0.58 3428
 
0.2%
0.53 3413
 
0.2%
0.61 3398
 
0.2%
0.54 3362
 
0.2%
0.55 3361
 
0.2%
0.46 3340
 
0.2%
Other values (1295) 1747877
97.8%
ValueCountFrequency (%)
0 8308
0.5%
0.001 1378
 
0.1%
0.002 1129
 
0.1%
0.003 1041
 
0.1%
0.004 900
 
0.1%
0.005 862
 
< 0.1%
0.006 790
 
< 0.1%
0.007 772
 
< 0.1%
0.008 753
 
< 0.1%
0.009 737
 
< 0.1%
ValueCountFrequency (%)
3.666 1
< 0.1%
1.93 1
< 0.1%
1.846 1
< 0.1%
1.828 1
< 0.1%
1.803 1
< 0.1%
1.777 1
< 0.1%
1.72 1
< 0.1%
1.669 1
< 0.1%
1.658 1
< 0.1%
1.62 1
< 0.1%

sub_grade
Categorical

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
C1
 
114557
B5
 
111747
B4
 
111161
C2
 
104058
B3
 
103952
Other values (30)
1241392 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3573734
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowB2
3rd rowB2
4th rowA3
5th rowB4

Common Values

ValueCountFrequency (%)
C1 114557
 
6.4%
B5 111747
 
6.3%
B4 111161
 
6.2%
C2 104058
 
5.8%
B3 103952
 
5.8%
C3 101008
 
5.7%
C4 100459
 
5.6%
B1 98596
 
5.5%
B2 98341
 
5.5%
C5 92768
 
5.2%
Other values (25) 750220
42.0%

Length

2024-07-09T22:23:30.130464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c1 114557
 
6.4%
b5 111747
 
6.3%
b4 111161
 
6.2%
c2 104058
 
5.8%
b3 103952
 
5.8%
c3 101008
 
5.7%
c4 100459
 
5.6%
b1 98596
 
5.5%
b2 98341
 
5.5%
c5 92768
 
5.2%
Other values (25) 750220
42.0%

Most occurring characters

ValueCountFrequency (%)
B 523797
14.7%
C 512850
14.4%
1 387814
10.9%
4 354600
9.9%
2 351577
9.8%
5 351426
9.8%
3 341450
9.6%
A 329095
9.2%
D 265392
7.4%
E 110811
 
3.1%
Other values (2) 44922
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 523797
14.7%
C 512850
14.4%
1 387814
10.9%
4 354600
9.9%
2 351577
9.8%
5 351426
9.8%
3 341450
9.6%
A 329095
9.2%
D 265392
7.4%
E 110811
 
3.1%
Other values (2) 44922
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 523797
14.7%
C 512850
14.4%
1 387814
10.9%
4 354600
9.9%
2 351577
9.8%
5 351426
9.8%
3 341450
9.6%
A 329095
9.2%
D 265392
7.4%
E 110811
 
3.1%
Other values (2) 44922
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 523797
14.7%
C 512850
14.4%
1 387814
10.9%
4 354600
9.9%
2 351577
9.8%
5 351426
9.8%
3 341450
9.6%
A 329095
9.2%
D 265392
7.4%
E 110811
 
3.1%
Other values (2) 44922
 
1.3%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
36
1333907 
60
452960 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3573734
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
36 1333907
74.7%
60 452960
 
25.3%

Length

2024-07-09T22:23:30.167132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T22:23:30.199747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
36 1333907
74.7%
60 452960
 
25.3%

Most occurring characters

ValueCountFrequency (%)
6 1786867
50.0%
3 1333907
37.3%
0 452960
 
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1786867
50.0%
3 1333907
37.3%
0 452960
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1786867
50.0%
3 1333907
37.3%
0 452960
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3573734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1786867
50.0%
3 1333907
37.3%
0 452960
 
12.7%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Source Verified
714425 
Not Verified
560906 
Verified
511536 

Length

Max length15
Median length12
Mean length12.054358
Min length8

Characters and Unicode

Total characters21539535
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowSource Verified
3rd rowVerified
4th rowNot Verified
5th rowNot Verified

Common Values

ValueCountFrequency (%)
Source Verified 714425
40.0%
Not Verified 560906
31.4%
Verified 511536
28.6%

Length

2024-07-09T22:23:30.238804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-09T22:23:30.276266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 1786867
58.4%
source 714425
 
23.3%
not 560906
 
18.3%

Most occurring characters

ValueCountFrequency (%)
e 4288159
19.9%
i 3573734
16.6%
r 2501292
11.6%
V 1786867
8.3%
f 1786867
8.3%
d 1786867
8.3%
o 1275331
 
5.9%
1275331
 
5.9%
S 714425
 
3.3%
u 714425
 
3.3%
Other values (3) 1836237
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21539535
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4288159
19.9%
i 3573734
16.6%
r 2501292
11.6%
V 1786867
8.3%
f 1786867
8.3%
d 1786867
8.3%
o 1275331
 
5.9%
1275331
 
5.9%
S 714425
 
3.3%
u 714425
 
3.3%
Other values (3) 1836237
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21539535
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4288159
19.9%
i 3573734
16.6%
r 2501292
11.6%
V 1786867
8.3%
f 1786867
8.3%
d 1786867
8.3%
o 1275331
 
5.9%
1275331
 
5.9%
S 714425
 
3.3%
u 714425
 
3.3%
Other values (3) 1836237
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21539535
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4288159
19.9%
i 3573734
16.6%
r 2501292
11.6%
V 1786867
8.3%
f 1786867
8.3%
d 1786867
8.3%
o 1275331
 
5.9%
1275331
 
5.9%
S 714425
 
3.3%
u 714425
 
3.3%
Other values (3) 1836237
8.5%

Interactions

2024-07-09T22:23:20.422093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.294645image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.850811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.210004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.773380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.333082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.853118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.243248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.919172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.483671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.888936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.655624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.343427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.003321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.431022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.020120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.527038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.437605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.935442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.364798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.869187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.420653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.936546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.341504image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.013721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.568082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.983347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.765874image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.448987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.095452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.525731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.108956image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.631534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.554583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.019478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.452534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.965411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.512699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.022377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.441269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.115123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.649403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.080637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.874722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.550719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.187462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.614928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.202739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.733429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.645507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.099498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.537388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.058113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.682438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.107408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.535827image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.213938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.729764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.179141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.984170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.652895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.275249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.708597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.284306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.836280image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.730755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.184408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.631594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.148989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.772712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.190658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.633622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.310948image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.815605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.277210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.089435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.755424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.361371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.797602image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.372213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.943759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.881563image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.267560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.731385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.249742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.861354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.279320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.733147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.412509image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.900289image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.386933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.191935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.864278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.446846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.887951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.459165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.041957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:57.963475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.345586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.823552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.348373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.947722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.357057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.830065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.505931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.979415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.490418image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.289147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.968442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.525546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.976259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.541411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.146135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.048116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.428833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.918377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.454043image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.044807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.443347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.925386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.606095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.067744image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.593367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.388411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.074735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.607799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.071454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.631678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.247175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.131446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.512253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.011520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.553473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.142467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.536490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.024902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.698553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.154184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.697574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.486145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.182431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.701552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.162065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.720599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.355439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.219152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.596348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.109313image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.652776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.230987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.625457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.135498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.802149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.235034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.790876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.592623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.289539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.795390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.253893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.811262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.461315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.313749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.689063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.214852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.760094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.320568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.716400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.241573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:08.911571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.329626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.886996image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.696592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.401064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.891185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.345005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.901979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.561397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.402478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.769652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.309939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.855644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.404457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.801322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.341712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.012525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.421132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:11.990516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.801282image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.495501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:16.982850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.429995image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:19.983007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.662152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.495104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.854243image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.407099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:02.952107image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.496871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.890090image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.537718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.110923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.518777image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.099721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:13.913233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.593608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.069706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.522488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.069820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.757339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.582695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:59.941158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.494711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.046466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.580622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:05.970139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.630066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.201038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.609875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.202377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.019177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.694072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.149119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.608702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.151930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.859699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.675860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.024641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.585887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.140940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.665713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.058644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.725326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.296851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.697554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.433262image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.127225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.797950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.240800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.834206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.237558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:21.951596image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:22:58.765196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:00.118855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:01.679529image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:03.244566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:04.766463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:06.150634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:07.825393image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:09.399713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:10.792622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:12.545940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:14.240887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:15.908201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:17.335804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:18.929558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-09T22:23:20.324552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-07-09T22:23:22.055136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-09T22:23:23.480633image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

acc_now_delinqaddr_stateannual_incapplication_typeavg_cur_balbc_utildelinq_2yrsdtiemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinq_last_6mthsinstallmentint_rateissue_dloan_amntloan_statuspub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermverification_status
425360.0NC60000.0Individual476.015.90.04.624PROJECT_MANAGER724.0720.0BRENT1.0392.810.10992013-12-0112000.0Fully Paid0.00.0debt_consolidation7137.00.240B236Not Verified
425370.0TX39600.0Individual1379.016.10.02.492SURGICAL_TECHNICIAN759.0755.0BMORTGAGE2.0157.130.10992013-12-014800.0Fully Paid0.00.0home_improvement4136.00.161B236Source Verified
425380.0MI55000.0Individual9570.053.90.022.8710TEAM_LEADERN_CUSTOMER_OPS_&_SYSTEMS734.0730.0BOWN0.0885.460.10992013-12-0127050.0Fully Paid0.00.0debt_consolidation36638.00.612B236Verified
425390.0TX96500.0Individual11783.083.50.012.613SYSTEMS_ENGINEER709.0705.0AMORTGAGE0.0373.940.07622013-12-0112000.0Fully Paid0.00.0debt_consolidation13248.00.557A336Not Verified
425400.0NC88000.0Individual2945.087.71.010.024ASSISTANT_DIRECTOR_-_HUMAN_RESOURCES674.0670.0BRENT0.0470.710.12852013-12-0114000.0Fully Paid1.01.0debt_consolidation3686.00.819B436Not Verified
425410.0CT105000.0Individual26765.025.00.014.0510MANAGER_INFORMATION_DELIVERY764.0760.0AMORTGAGE1.0368.450.06622013-12-0112000.0Fully Paid0.00.0debt_consolidation13168.00.216A236Not Verified
425420.0FL63000.0Individual38927.079.10.016.512AIRCRAFT_MAINTENANCE_ENGINEER674.0670.0AMORTGAGE0.0476.300.08902013-12-0115000.0Fully Paid0.00.0debt_consolidation11431.00.742A536Not Verified
425430.0CA28000.0Individual1440.096.00.08.403SPECIAL_ORDER_FULFILLMENT_CLERK664.0660.0CRENT0.0266.340.16242013-12-017550.0Fully Paid0.00.0debt_consolidation5759.00.720C536Not Verified
425440.0CA325000.0Individual53306.067.10.018.555AREA_SALES_MANAGER749.0745.0AMORTGAGE1.0872.520.07622013-12-0128000.0Fully Paid0.00.0debt_consolidation29581.00.546A336Source Verified
425450.0CO130000.0Individual36362.093.00.013.0310LTC719.0715.0BMORTGAGE1.0398.520.11992013-12-0112000.0Fully Paid0.00.0debt_consolidation10805.00.670B336Source Verified
acc_now_delinqaddr_stateannual_incapplication_typeavg_cur_balbc_utildelinq_2yrsdtiemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinq_last_6mthsinstallmentint_rateissue_dloan_amntloan_statuspub_recpub_rec_bankruptciespurposerevol_balrevol_utilsub_gradetermverification_status
29254830.0FL32000.0Joint App2331.052.90.070.924SALES_MANAGER709.0705.0CMORTGAGE1.0683.940.13992017-04-0129400.0Fully Paid0.00.0debt_consolidation12264.00.461C360Source Verified
29254840.0MD180000.0Individual63816.068.00.010.415BRANCH_MANAGER679.0675.0EOWN4.01037.380.25492017-04-0135000.0Fully Paid0.00.0debt_consolidation31233.00.580E460Source Verified
29254850.0CA64500.0Individual11056.067.30.09.6610FOREMAN664.0660.0FMORTGAGE3.0378.650.28692017-04-0112000.0Fully Paid1.01.0home_improvement16478.00.513F160Not Verified
29254860.0IL100000.0Individual28408.014.20.08.320INDUSTRIAL_ENGINEER689.0685.0CMORTGAGE1.0814.210.13992017-04-0135000.0Fully Paid1.01.0debt_consolidation3162.00.142C360Source Verified
29254870.0CA53000.0Individual4113.092.10.024.650NaN664.0660.0ERENT0.0538.400.22742017-04-0119200.0Non-Performing0.00.0debt_consolidation23058.00.769E160Source Verified
29254880.0CO107000.0Individual5528.026.03.011.650SENIOR_ESCROW_OFFICER674.0670.0ERENT1.0690.300.23992017-04-0124000.0Non-Performing2.01.0other9688.00.249E260Source Verified
29254890.0PA65000.0Individual3724.024.61.019.5510NURSE729.0725.0AMORTGAGE0.0313.320.07992017-04-0110000.0Fully Paid0.00.0debt_consolidation9751.00.157A536Source Verified
29254900.0VA37000.0Individual1021.066.10.020.568SALES_ASSOCIATE709.0705.0DRENT1.0358.260.16992017-04-0110050.0Non-Performing0.00.0debt_consolidation14300.00.470D136Not Verified
29254911.0NY41000.0Individual3275.05.51.019.995CONTACT_INPUT674.0670.0BRENT0.0197.690.11442017-04-016000.0Fully Paid0.00.0credit_card1356.00.101B436Source Verified
29254920.0TX105700.0Individual19955.080.61.027.264ASSISTANT_MANAGER699.0695.0EMORTGAGE0.0889.180.25492017-04-0130000.0Non-Performing0.00.0debt_consolidation15252.00.726E460Verified